Improving Itinerary Recommendations for Tourists Through Metaheuristic Algorithms: An Optimization Proposal

In recent years, recommender systems have been used as a solution to support tourists with recommendations oriented to maximize the entertainment value of visiting a tourist destination. However, this is not an easy task because many aspects need to be considered to make realistic recommendations: the context of a tourist destination visited, lack of updated information about points of interest, transport information, weather forecast, etc. The recommendations concerning a tourist destination must be linked to the interests and constraints of the tourist. In this research, we present a mobile recommender system based on Tourist Trip Design Problem (TTDP)/Time Depending (TD) – Orienteering Problem (OP) – Time Windows (TW), which analyzes in real time the user’s constraints and the points of interest’s constraints. For solving TTDP, we clustered preferences depending on the number of days that a tourist will visit a tourist destination using a k-means algorithm. Then, with a genetic algorithm (GA), we optimize the proposed itineraries to tourists for facilitating the organization of their visits. We also used a parametrized fitness function to include any element of the context to generate an optimized recommendation. Our recommender is different from others because it is scalable and adaptable to environmental changes and users’ interests, and it offers real-time recommendations. To test our recommender, we developed an application that uses our algorithm. Finally, 131 tourists used this recommender system and an analysis of users’ perceptions was developed. Metrics were also used to detect the percentage of precision, in order to determine the degree of accuracy of the recommender system. This study has implications for researchers interested in developing software to recommend the best itinerary for tourists with constraint controls with regard to the optimized itineraries

BTRec: BERT-Based Trajectory Recommendation for Personalized Tours

An essential task for tourists having a pleasant holiday is to have a well-planned itinerary with relevant recommendations, especially when visiting unfamiliar cities. Many tour recommendation tools only take into account a limited number of factors, such as popular Points of Interest (POIs) and routing constraints. Consequently, the solutions they provide may not always align with the individual users of the system. We propose an iterative algorithm in this paper, namely: BTREC (BERT-based Trajectory Recommendation), that extends from the POIBERT embedding algorithm to recommend personalized itineraries on POIs using the BERT framework. Our BTREC algorithm incorporates users' demographic information alongside past POI visits into a modified BERT language model to recommend a personalized POI itinerary prediction given a pair of source and destination POIs. Our recommendation system can create a travel itinerary that maximizes POIs visited, while also taking into account user preferences for categories of POIs and time availability. Our recommendation algorithm is largely inspired by the problem of sentence completion in natural language processing (NLP). Using a dataset of eight cities of different sizes, our experimental results demonstrate that our proposed algorithm is stable and outperforms many other sequence prediction algorithms, measured by recall, precision, and F1-scores.Comment: RecSys 2023, Workshop on Recommenders in Touris

Considering temporal aspects in recommender systems: a survey

Under embargo until: 2023-07-04The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in various domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multiple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering temporal aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal aspects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a temporal abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.acceptedVersio

Applying reranking strategies to route recommendation using sequence-aware evaluation

Venue recommendation approaches have become particularly useful nowadays due to the increasing number of users registered in location-based social networks (LBSNs), applications where it is possible to share the venues someone has visited and establish connections with other users in the system. Besides, the venue recommendation problem has certain characteristics that differ from traditional recommendation, and it can also benefit from other contextual aspects to not only recommend independent venues, but complete routes or venue sequences of related locations. Hence, in this paper, we investigate the problem of route recommendation under the perspective of generating a sequence of meaningful locations for the users, by analyzing both their personal interests and the intrinsic relationships between the venues. We divide this problem into three stages, proposing general solutions to each case: First, we state a general methodology to derive user routes from LBSNs datasets that can be applied in as many scenarios as possible; second, we define a reranking framework that generate sequences of items from recommendation lists using different techniques; and third, we propose an evaluation metric that captures both accuracy and sequentiality at the same time. We report our experiments on several LBSNs datasets and by means of different recommendation quality metrics and algorithms. As a result, we have found that classical recommender systems are comparable to specifically tailored algorithms for this task, although exploiting the temporal dimension, in general, helps on improving the performance of these techniques; additionally, the proposed reranking strategies show promising results in terms of finding a trade-off between relevance, sequentiality, and distance, essential dimensions in both venue and route recommendation tasksThis work has been funded by the Ministerio de Ciencia, Innovación y Universidades (reference: TIN2016-80630-P) and by the European Social Fund (ESF), within the 2017 call for predoctoral contract

POIBERT: A Transformer-based Model for the Tour Recommendation Problem

Tour itinerary planning and recommendation are challenging problems for tourists visiting unfamiliar cities. Many tour recommendation algorithms only consider factors such as the location and popularity of Points of Interest (POIs) but their solutions may not align well with the user's own preferences and other location constraints. Additionally, these solutions do not take into consideration of the users' preference based on their past POIs selection. In this paper, we propose POIBERT, an algorithm for recommending personalized itineraries using the BERT language model on POIs. POIBERT builds upon the highly successful BERT language model with the novel adaptation of a language model to our itinerary recommendation task, alongside an iterative approach to generate consecutive POIs. Our recommendation algorithm is able to generate a sequence of POIs that optimizes time and users' preference in POI categories based on past trajectories from similar tourists. Our tour recommendation algorithm is modeled by adapting the itinerary recommendation problem to the sentence completion problem in natural language processing (NLP). We also innovate an iterative algorithm to generate travel itineraries that satisfies the time constraints which is most likely from past trajectories. Using a Flickr dataset of seven cities, experimental results show that our algorithm out-performs many sequence prediction algorithms based on measures in recall, precision and F1-scores.Comment: Accepted to the 2022 IEEE International Conference on Big Data (BigData2022

VoyageWithUs : a recommender platform that enhances group travel planning

Group travel planning poses unique challenges such as choosing hotels, restaurants and venues while catering to everyone’s wants and needs, or sharing trip itineraries and artifacts among trip participants. State of the art travel planning applications such as Yelp and TripAdvisor, while integrating with social networks and making recommendations, don’t offer recommendations for specific groups of travelers. On the other hand, while TripCase offers trip planning capabilities and email sharing, it doesn’t offer a full interactive travel planner that allows groups to contribute to the travel planning process. This report proposes an approach to making personalized group travel recommendations based on hybrid recommendation techniques that aggregates individual recommendations to find common ground between trip participants. This is achieved by designing a recommender system that uses data from a location based social network(LBSN) and makes recommendations based on the trip location, then refines them by applying incremental filters which are responsible for incorporating user preferences, similarity to other users and user context. Finally, it takes the generated recommendations for each trip participant and ranks them such that the items most highly ranked are the ones most likely to fit everyone’s preferences. The rationale for choosing a hybrid recommender system is to address common issues such as the cold start problem, where the quality of the recommendations is affected by either too few reviewers for a certain point of interest(POI) or too few reviews generated by trip participants. These issues, along with a coverage of related work is detailed in the first part of this report. In order to make the applicability of the recommender more tangible, I integrated it into a proof of concept mobile application that also allows travelers to collaborate and share travel planning artifacts, and generates itineraries based on the recommendations made. The recommender accuracy was measured against recommendations made by state of the art applications, while individual filters were evaluated using commonly used metrics. The recommender was tested in a series of relevant scenarios proving the effectiveness of the approach in making group travel recommendations, versus individual recommendations generated by other applications.Electrical and Computer Engineerin

Recommender system for personalised travel itinerary

A recommender system is an approach to give an appropriate solu-tion to a particular problem. This helps in recognising the pattern or behaviour of a user to suggest future possible likes of the user. Nowa-days people like to travel during their spare time, it has become a rigid task to decide where to go. This paper represents a customised recommender system to help users in destining their itinerary. A model is designed to suggest the best places to visit in Rome. A questionnaire was prepared to get information about users interest during their travel. The model generates the best five places to visit with respect to the choice picked by the user. The top five places for each category will be displayed to the user and the user was asked to pick a starting point for the itinerary. Then the model generates another set off a filtered list of places to enhance their travel experi-ence. It includes displaying the top 5 restaurants to visit during their travel

Recommending places blased on the wisdom-of-the-crowd

The collective opinion of a great number of users, popularly known as wisdom of the crowd, has been seen as powerful tool for solving problems. As suggested by Surowiecki in his books [134], large groups of people are now considered smarter than an elite few, regardless of how brilliant at solving problems or coming to wise decisions they are. This phenomenon together with the availability of a huge amount of data on the Web has propitiated the development of solutions which employ the wisdom-of-the-crowd to solve a variety of problems in different domains, such as recommender systems [128], social networks [100] and combinatorial problems [152, 151]. The vast majority of data on the Web has been generated in the last few years by billions of users around the globe using their mobile devices and web applications, mainly on social networks. This information carries astonishing details of daily activities ranging from urban mobility and tourism behavior, to emotions and interests. The largest social network nowadays is Facebook, which in December 2015 had incredible 1.31 billion mobile active users, 4.5 billion “likes” generated daily. In addition, every 60 seconds 510 comments are posted, 293, 000 statuses are updated, and 136,000 photos are uploaded1. This flood of data has brought great opportunities to discover individual and collective preferences, and use this information to offer services to meet people’s needs, such as recommending relevant and interesting items (e.g. news, places, movies). Furthermore, it is now possible to exploit the experiences of groups of people as a collective behavior so as to augment the experience of other. This latter illustrates the important scenario where the discovery of collective behavioral patterns, the wisdom-of-the-crowd, may enrich the experience of individual users. In this light, this thesis has the objective of taking advantage of the wisdom of the crowd in order to better understand human mobility behavior so as to achieve the final purpose of supporting users (e.g. people) by providing intelligent and effective recommendations. We accomplish this objective by following three main lines of investigation as discussed below. In the first line of investigation we conduct a study of human mobility using the wisdom-of- the-crowd, culminating in the development of an analytical framework that offers a methodology to understand how the points of interest (PoIs) in a city are related to each other on the basis of the displacement of people. We experimented our methodology by using the PoI network topology to identify new classes of points of interest based on visiting patterns, spatial displacement from one PoI to another as well as popularity of the PoIs. Important relationships between PoIs are mined by discovering communities (groups) of PoIs that are closely related to each other based on user movements, where different analytical metrics are proposed to better understand such a perspective. The second line of investigation exploits the wisdom-of-the-crowd collected through user-generated content to recommend itineraries in tourist cities. To this end, we propose an unsupervised framework, called TripBuilder, that leverages large collections of Flickr photos, as the wisdom-of- the-crowd, and points of interest from Wikipedia in order to support tourists in planning their visits to the cities. We extensively experimented our framework using real data, thus demonstrating the effectiveness and efficiency of the proposal. Based on the theoretical framework, we designed and developed a platform encompassing the main features required to create personalized sightseeing tours. This platform has received significant interest within the research community, since it is recognized as crucial to understand the needs of tourists when they are planning a visit to a new city. Consequently this led to outstanding scientific results. In the third line of investigation, we exploit the wisdom-of-the-crowd to leverage recommendations of groups of people (e.g. friends) who can enjoy an item (e.g. restaurant) together. We propose GroupFinder to address the novel user-item group formation problem aimed at recommending the best group of friends for a pair. The proposal combines user-item relevance information with the user’s social network (ego network), while trying to balance the satisfaction of all the members of the group for the item with the intra-group relationships. Algorithmic solutions are proposed and experimented in the location-based recommendation domain by using four publicly available Location-Based Social Network (LBSN) datasets, showing that our solution is effective and outperforms strong baselines